Achieving shrinkage in a time-varying parameter model framework

被引：72

作者：

Bitto, Angela ^{[1
]}

Fruehwirth-Schnatter, Sylvia ^{[1
]}

机构：

[1] WU Vienna Univ Econ & Business, Inst Stat & Math, Dept Finance Accounting & Stat, Vienna, Austria

来源：

JOURNAL OF ECONOMETRICS | 2019年 / 210卷 / 01期

关键词：

Bayesian inference; Bayesian Lasso; Double gamma prior; Hierarchical priors; Kalman filter; Log predictive density scores; Normal-gamma prior; Sparsity; State space model; INTERWEAVING STRATEGY ASIS; STOCHASTIC VOLATILITY; HIERARCHICAL SHRINKAGE; VARIABLE SELECTION; BAYESIAN-ANALYSIS; INFERENCE; PREDICTION;

D O I：

10.1016/j.jeconom.2018.11.006

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying parameters to static ones, if the model is overfitting. This is achieved through placing the double gamma shrinkage prior on the process variances. An efficient Markov chain Monte Carlo scheme is developed, exploiting boosting based on the ancillarity-sufficiency interweaving strategy. The method is applicable both to TVP models for univariate as well as multivariate time series. Applications include a TVP generalized Phillips curve for EU area inflation modeling and a multivariate TVP Cholesky stochastic volatility model for joint modeling of the returns from the DAX-30 index. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：75 / 97

页数：23

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